3 Methods of Marketing Attribution Analysis

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One of the simplest, yet most powerful ways to perform attribution analysis as a marketer is to ask people in an open-ended way how they heard of you. My friend and colleague Tom Webster recommends asking, “What made you contact us today?” as a variation.

This creates a challenge for many marketers. What we receive is unstructured data, data that looks like this:

  • I heard about you from a friend
  • Saw you at Social Media Marketing World
  • Chris Brogan mentioned in newsletter
  • Don’t remember
  • Don’t know
  • asdkjfhsdklfhsdkjfh (yes, someone wrote that)

The challenge many marketers face with unstructured data is analyzing it. Unstructured data, by definition, is messy to work with. However, it’s not beyond our ability to process if we have the right tools and methods. Let’s look at three methods for analyzing unstructured data.


The easiest and most straightforward way to analyze unstructured data is the word frequency count. How many times, in a list of responses from prospects, does the word Google occur?

While this method is fast and easy, it’s not necessarily as accurate as we’d like. Simple word counts mislead us; someone who replies, “First I Googled for you and then I saw you at a show” could mistakenly be attributed only to Google. However, word counts are better than no analysis at all.


Using visualization software like Tableau or Watson Analytics, start by clustering logical data points together. Suppose in our text responses we saw a collection which looked like this:

  • Google
  • I googled you
  • Google.com
  • Searched w Google
  • https://www.google.com

We would logically cluster these responses together as Google. If you know your sources and referring sites well, constructing a clustered tree map is straightforward (albeit time-intensive):

Clustered Treemap of Attribution.png

Above, we see hundreds of different variations of Google, Twitter, and Facebook clustered together with manual analysis. What’s interesting is that Twitter has more variations of responses than Facebook, but aggregated together, Twitter is more impactful than Facebook.


Using artificial intelligence, feed your responses into the AI and have it determine what’s being said. IBM Watson and Google Cloud Natural Language services allow us to extract keywords, clusters, phrases, and entities from large bodies of unstructured text, transforming human language into quantitative data sets. For most marketers, this is likely overkill, but for enterprises with a large amount of text data to process, this may be the best choice.

Do Something With Your Unstructured Data

Whether you choose the easy, medium, or hard options, do something with your unstructured data. It’s a nearly-literal gold mine that you’re probably not tapping appropriately, and the treasure could be insights that truly change your marketing.

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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.


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